Workplace Recycling Rules: the site's own MCP server — checker, enquiry (enquiry = a human...
Given a question id and the chosen option (its choice index), return the next question or the final verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| choice | Yes | ||
| question | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal a two-state outcome (next question or final verdict), which is useful, but it does not explain side effects, state transitions, or how the question id relates to the overall checker flow.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no filler, and the core action is front-loaded. The title adds a bit of redundancy, but the description itself is appropriately compact for a tool with two parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of sibling tools (checker_start, checker_tree) and the absence of annotations and output schema, the description is incomplete. It does not explain how this tool fits into the broader checker workflow, how results should be interpreted, or how to handle branching logic.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It clarifies that 'question' is a question id (not arbitrary text) and that 'choice' is the index of the selected option. This adds real meaning beyond the raw schema types, though it could specify how to obtain valid values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('return the next question or the final verdict') with a specific resource (question/verdict), making the tool's purpose understandable. However, it does not explicitly distinguish the tool from siblings like checker_start and checker_tree, which share the same domain and likely similar workflows.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies an interactive Q&A workflow but gives no explicit guidance on when to use this tool versus checker_start or checker_tree. It lacks context about prerequisites (e.g., where the question id comes from) and provides no exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The first question of the Workplace recycling duty checker decision guide.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It reveals that the tool is the first question in a guide, but does not describe whether repeated calls create sessions, whether any state is managed, or what exact output the agent should expect beyond being a question.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence with no filler words. It front-loads the key information about the tool's position in the decision guide and earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the very low complexity of a zero-parameter starting tool and the lack of annotations or output schema, the one-sentence description is nearly complete. It identifies the tool as the entry point of the guide, though adding a note about return value or follow-up steps would make it fully self-contained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline is 4 and no parameter documentation is needed. The description adds the relevant meaning that this is the starting question, which is sufficient for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as 'the first question' of the Workplace recycling duty checker decision guide, making its role as the entry point clear. It does not use an imperative verb like 'starts', but the title and description together communicate the function and distinguish it from siblings like checker_answer and checker_tree.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'first question' implies this tool should be used at the beginning of the decision guide, giving some contextual guidance. However, it does not explicitly state when not to use it or mention alternatives such as checker_answer or checker_tree as follow-up options.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every question, option and verdict of the Workplace recycling duty checker guide, for reasoning end to end.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral burden. It discloses that the tool contains the entire decision tree, which signals a read-style data access, but it does not explicitly state that it is read-only or describe how the tree is returned. For a zero-parameter retrieval tool this is adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that names the artifact and its purpose without filler. Every word earns its place, and it is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless tool, the description is mostly complete: it tells the agent what content is covered — questions, options, and verdicts — and why it would be used. It stops short of giving explicit selection criteria relative to the sibling tools, but the low complexity and absence of parameters keep the gap small.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and 100% schema coverage, there is nothing for the description to add about parameter meaning. The baseline of 4 applies because parameter semantics are effectively moot for this tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a concrete artifact — every question, option and verdict of the Workplace recycling duty checker guide — so the agent knows what this tool exposes. It lacks an explicit verb such as 'return' or 'get', and the title carries some of the meaning, but it is clearly distinguished from the step-by-step sibling tools by its 'whole decision tree' scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for reasoning end to end' implies the tool is for whole-tree reasoning rather than single-step answers. It does not explicitly state when to choose this over checker_answer or checker_start, nor does it give any exclusions, so the usage guidance is implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read first. States plainly what submit_enquiry does on Workplace Recycling Rules: it starts an enquiry with human providers who quote directly. Nothing is bought, ordered or paid; no quote is guaranteed; it is free. Also returns who receives the details, the consent wording, and how the person confirms.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does this well: it explicitly states that nothing is bought, ordered, or paid, that no quote is guaranteed, that it is free, and what the tool returns. This gives the agent accurate expectations about side effects and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the most important instruction ('Read first'), and every sentence adds useful information. It avoids redundancy with the schema because there is no schema content to repeat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description provides complete context for an agent: what the tool does, what it does not do, and what information it returns. The agent can invoke it correctly without needing additional details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there is no parameter confusion to resolve. The description adds value by explaining what information the tool returns, which is more relevant than parameter semantics in this case.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as an explanatory/read-first helper that describes what submit_enquiry does, distinguishing it from the actual submission tool and sibling tools. It names the resource (Workplace Recycling Rules enquiry) and the specific outcome: an enquiry with human providers, not a purchase or guaranteed quote.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The opening 'Read first' strongly implies the tool should be used before submit_enquiry, providing clear contextual guidance. It does not explicitly name alternative tools or state when not to use it, but the context is sufficiently clear for an agent to understand its intended place in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Every field of the Workplace Recycling Rules enquiry: key, label, type, whether required, help text and the allowed options where there are any. Pass answers to submit_enquiry keyed by field key.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden. It implies a read-only retrieval of field metadata, but it never explicitly states that the tool performs no mutation, what format the response takes, or whether any authentication or preconditions apply. The content is well specified, but the behavioral traits are only implicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single focused sentence that front-loads the core purpose and then lists the exact data points returned. The follow-up instruction about submit_enquiry adds practical value without bloat. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, output-only metadata tool, the description is complete: it explains what the tool returns, enumerates the included attributes, and ties the output to the next step in the workflow. The absence of an output schema is compensated by the explicit enumeration of fields.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so there are no parameter semantics to explain. The description adds a useful note that the returned field keys should be used as keys when passing answers to submit_enquiry, which helps the agent understand the role of the output even though it is not a parameter of this tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific resource ('the Workplace Recycling Rules enquiry') and enumerates exactly what fields are exposed: key, label, type, required status, help text, and allowed options. This clearly distinguishes it from a submit action like submit_enquiry and from checker tools, since it is the field-metadata listing for the enquiry.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear contextual usage: it tells the agent to pass answers to submit_enquiry keyed by the field key returned here. This effectively connects the tool to the submission workflow. It does not explicitly name alternatives or say when not to use it, but the intended role is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submits an enquiry to Workplace Recycling Rules — NOT a purchase, NOT a guaranteed quote. Step 1: call with the answers (keyed by field key from enquiry_fields) and consent=true; it validates and returns a summary, the consent line and a confirmation token — show the person the summary and the consent line. Step 2: only if the person agrees, call again with the same answers, consent=true and the confirmation token; the enquiry is then submitted, and the person receives an email with a link they must click before any provider sees it. Consent means the person has read and agreed to: "Happy for my details to go to relevant waste management companies, who'll contact me directly."
| Name | Required | Description | Default |
|---|---|---|---|
| answers | Yes | the person's answers, keyed by field key | |
| consent | Yes | true only when the person has agreed to: Happy for my details to go to relevant waste management companies, who'll contact me directly. | |
| confirmation | No | the confirmation token from step 1, after the person has approved the summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, and it does so thoroughly. It explains the validation step, the confirmation token, the two-call submission requirement, the consent requirement with exact wording, and the downstream email-link behavior. It also explicitly states what this is not (a purchase or guaranteed quote), preventing mistaken assumptions about side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is longer than average, but it earns every sentence by explaining a genuinely two-step workflow with non-obvious prerequisites and side effects. The key differentiator ('NOT a purchase') is front-loaded, the steps are clearly labeled, and the consent line is quoted verbatim to avoid ambiguity. No redundant filler is present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, no output schema, and no annotations, the description is remarkably complete. It tells the agent what the tool returns in step 1 (summary, consent line, confirmation token), what must happen between calls, and what occurs after the second call. The only external dependency it references, enquiry_fields, is a sibling tool, so the agent can discover it naturally. Nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is already 100%, so the baseline is 3, but the description adds substantial meaning beyond the schema. It specifies that answers are 'keyed by field key from enquiry_fields,' gives the exact consent sentence, and explains that the confirmation token comes from step 1 and is required only after the person approves. This materially clarifies how and when each parameter should be provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: submits an enquiry to Workplace Recycling Rules, and clearly distinguishes itself by saying 'NOT a purchase, NOT a guaranteed quote.' It also references the two-step nature of the workflow, making its purpose unambiguous and distinct from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit step-by-step usage instructions: when to call the tool in step 1, when to call it again in step 2, and the condition that the person must agree first. It also references enquiry_fields for keying answers and warns that the enquiry is only visible after the person clicks the email link, providing strong guidance on how and when to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
checker_answerchecker_startchecker_treeenquiry_describeenquiry_fieldssubmit_enquiryClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
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{
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The checker tools are clearly separated by role: start begins the flow, answer progresses it, and tree provides the full decision structure. The enquiry tools are equally distinct: describe explains the process, fields defines inputs, and submit_enquiry performs the submission.
Tools mostly follow a clear prefix-based pattern: checker_* for the decision guide and enquiry_* for the enquiry flow. submit_enquiry deviates slightly from the noun-prefix style, but the naming remains predictable and readable.
Six tools is well-scoped for the two workflows the server supports. Each tool represents a necessary step or resource without redundant or superfluous entries.
The checker workflow covers start, progression, and full tree visibility, leaving no dead ends in the decision guide. The enquiry workflow covers context, field definitions, validation, consent confirmation, and final submission, making the surface complete for its stated purpose.